Mastering-Pandas-DataFrames-The-Ultimate-Guide-for-CBSE-Class-XII-Informatics-Practices-Code-065

• df.index: Returns the row labels (index) of the DataFrame.
• df.columns: Returns the column labels of the DataFrame.
• df.axes: Returns a list containing both the row axis labels and column axis labels.
• df.dtypes: Returns the data type of each column.
• df.size: Returns the total number of elements (Rows × Columns).
• df.shape: Returns a tuple representing the dimensions (rows, columns).
• df.values: Returns a NumPy ndarray representation of the data.
• df.empty: Returns True if the DataFrame is completely empty; otherwise False.
• df.T (Transpose): Flips the DataFrame, swapping its rows with its columns. 

# Displays the top 2 rows
print(df.head(2))

# Displays the bottom 3 rows
print(df.tail(3))

# Syntax: df.loc[row_label, column_label]
# Selects rows from 'Row1' to 'Row3' and only the 'Salary' column
print(df.loc['Row1':'Row3', 'Salary'])

# Syntax: df.iloc[row_position, column_position]
# Selects the first 2 rows and the first 3 columns
print(df.iloc[0:2, 0:3])

# Filters rows where the Marks column is greater than 90
print(df[df['Marks'] > 90])

• Selecting a Column: df['Column_Name']
• Adding a New Column: Assign values directly to a new key.

  df['Total_Marks'] = 100  # Sets all rows to 100
  

• df.drop(): Used to drop columns or rows. For columns, students must specify axis=1.

  # Deleting a column
  df_new = df.drop('Age', axis=1)
  
  # Deleting a row by label
  df_new = df.drop('Row0', axis=0)
  

• del Statement: Permanently removes a column in-place.

  del df['Age']

• df.rename(): Alters row index labels or column labels.

  # Renaming a column header
  df.rename(columns={'OldName': 'NewName'}, inplace=True)

for idx, row in df.iterrows():
    print(f"Row Index: {idx}")
    print(f"Student Name: {row['Name']}")

for col_name, col_data in df.items():
    print(f"Column Name: {col_name}")
    print(col_data)

# Reading a CSV file
df = pd.read_csv('student_records.csv')

# Exporting without saving row index numbers
df.to_csv('output_data.csv', index=False)